Method for correcting defects and in particular for reducing noise in an image provided by an image sensor

ABSTRACT

A method of correcting defects appearing in an image produced by an image sensor, the method comprising: receiving an image to be corrected, taken by the image sensor, receiving a temperature from the image sensor, acquired when the image to be corrected is taken by the image sensor, receiving an integration time applied by the image sensor when taking the image to be corrected, and for each pixel of the image to be corrected, subtracting from the pixel value a pixel-specific noise correction factor derived from a noise reduction model comprising a linear component dependent on the temperature of the image sensor, added to an exponential component depending on the temperature of the image sensor and multiplied by the integration time, the linear and exponential components depending on coefficients specific to the pixel.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims priority under 35 U.S.C. § 119to French Patent Application No. 20 00834 filed on Jan. 28, 2020, thedisclosure of which is herein incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of imaging devices such asvideo and still cameras. The disclosure relates in particular to imagingdevices integrating image sensors in CMOS (Complementary Metal OxideSemiconductor) or CCD (Charge-Coupled Device) technology, of the CTIA(Charge Trans Impedance Amplifier) or SF (Source Follower) type. Thedisclosure can be applied to the field of imaging in the visible range,as well as in SWIR, MWIR, LWIR (Short-, Mid-, Long-Wave Infra-Red)ranges.

BACKGROUND

Typically, a CMOS image sensor consists of pixels or photosites arrangedin an array configuration. Each pixel includes a photosensitive area,usually a photodiode, configured to accumulate electric chargesaccording to the light it receives, and a readout circuit to measure theamount of charges accumulated by the photodiode. The readout circuitincludes a transfer transistor to control the transfer of the electriccharges accumulated in the photodiode to a readout node. The pixel isthus controlled according to a cycle comprising an initialization phase,an integration phase, and a readout phase. During the integration phase,the photodiode accumulates electric charges according to the light itreceives. The readout phase includes generating a signal correspondingto the amount of electric charges accumulated by the photodiode duringthe integration phase. The initialization phase includes eliminating theelectric charges accumulated by the photodiode during the integrationphase.

The images produced by today's image sensors are disturbed by varioussources of noise, especially when the image sensors are miniaturized.The effects of some of these noise sources appear in the images takenwhen the image sensor is placed in the dark. Under these conditions, thepixel circuits of the image sensor may produce signals although thepixels are completely dark. It turns out that the amplitude of thesedark signals varies with the temperature of the image sensor, and theintegration time selected to generate the image. The amplitude of thesedark signals can also vary from one pixel circuit to another within thesame image sensor, and from one image sensor to another, even if theyare from the same manufacturing batch.

It is known to establish a Fixed Pattern Noise image by generating adark image using the image sensor placed in the dark, and to subtractthis dark image from the images produced by the image sensor. To accountfor temperature variations and integration time, it would be necessaryto produce such a dark image each time a new image is to be acquired.However, in many applications, especially video capture, the timeavailable before each frame is usually insufficient to generate such adark image. In addition, applying a correction to each frame or seriesof frames in the case of video images inevitably generates a flickerthat can be upsetting.

It is therefore desirable to provide an efficient noise reduction methodin an imaging device, which takes into account variations in imagesensor temperature and changes in integration time, and which does notdisturb the viewing experience. It is also desirable that the noisereduction method be adaptable to each imaging device.

SUMMARY

Embodiments relate to a method of correcting defects appearing in animage produced by an image sensor, the method comprising the steps of:receiving an image to be corrected, taken by the image sensor, receivinga temperature from the image sensor, acquired when the image to becorrected is taken by the image sensor, receiving an integration timeapplied by the image sensor when taking the image to be corrected, andfor each pixel of the image to be corrected, subtracting from the pixelvalue a pixel-specific noise correction factor derived from a noisereduction model comprising a linear component dependent on thetemperature of the image sensor, added to an exponential componentdepending on the temperature of the image sensor and multiplied by theintegration time, the linear and exponential components depending oncoefficients specific to the pixel.

According to an embodiment, the noise reduction model is defined by thefollowing equation:

bm[i,j]=IT×ad[i,j]×Exp(bd[i,j]×TP)+ab[i,j]×TP+bb[i,j]

where bm[i,j] is the noise correction factor to be subtracted from acorresponding pixel of the image to be corrected, IT is the integrationtime, TP is the temperature of the image sensor, EXP is the exponentialfunction, and ad[i,j], bd[i,j], ab[i,j] and bb[i,j] are thepixel-specific coefficients.

According to an embodiment, the method comprises calculating the noisecorrection factor of each pixel each time the integration time ischanged, or each time the temperature of the image sensor deviates froma previous value by more than a temperature deviation threshold value.

According to an embodiment, the method comprises the steps of: acquiringby the image sensor images in the absence of light with a minimumintegration time, each image being taken at a distinct respectivetemperature, and determining, for each pixel of the image to becorrected, the coefficients of the linear component, by linearregression calculations applied to corresponding pixels in the images inthe absence of light taken at the respective temperatures.

According to an embodiment, the method comprises the steps of: acquiringby the image sensor images in the absence of light, at differentintegration times, the image sensor being subjected to differenttemperatures, generating corrected images obtained by subtracting fromeach of the images taken by the image sensor in the absence of light atdifferent integration times and at different temperatures, the imagetaken by the image sensor in the absence of light with the minimumintegration time and at the same temperature, and determining thecoefficients of the exponential component by exponential fittingcalculations applied to the respective pixels of the corrected imagesobtained at different temperatures and corresponding to the sameintegration time.

According to an embodiment, the coefficients of the exponentialcomponent are determined by averaging coefficients obtained byexponential fitting calculations for different integration times.

According to an embodiment, the noise reduction model comprises anidentical component for all pixels of the image sensor depending on theintegration time.

According to an embodiment, the method comprises acquiring a stream ofvideo images, wherein the noise correction factor corresponding to eachpixel of an image of the video stream is subtracted from thecorresponding pixel of each image of the video stream.

According to an embodiment, the method comprises the steps of: receivinga command to select a gain value of the image sensor, and selecting foreach pixel of the image to be corrected a pixel-specific noisecorrection factor as a function of the selected gain value, the noisecorrection factor being used to correct the value of each pixel of theimage to be corrected, the noise correction factor being determined froma set of pixel-specific coefficients generated as a function of theselected gain value.

According to an embodiment, the method comprises, for each pixel of acorrected image after a noise reduction, multiplying the value of thepixel by a gain correction factor specific to the pixel, taken from again normalization table, to obtain an image with a normalized gain.

According to an embodiment, the method comprises updating the gainnormalization table each time the integration time is changed, or eachtime the temperature of the image sensor deviates from a previous valueby more than a temperature deviation threshold value, the updating ofthe gain normalization table being carried out by interpolationcalculations applied to a set of gain normalization tables determinedfor different temperatures.

According to an embodiment, the method comprises the steps of: acquiringby the image sensor a series of images in the presence of a uniformlight source with different integration times or at differentintensities of the light source, each series of images being taken at adifferent respective temperature, determining a gain, for each pixel ofone of the images of each image series, by a linear regressioncalculation applied to corresponding pixels in the images of the imageseries, and determining, for each pixel of one of the images of eachimage series, a gain correction factor by dividing an average of thegains obtained for all the pixels of the image series by the gaindetermined for the pixel.

According to an embodiment, the method comprises the steps of: acquiringby the image sensor a series of images in the absence of light, at anaverage integration time, each image being obtained with the imagesensor subjected to a respective temperature, calculating, for eachpixel of one of the images of the acquired image series, an average ofdeviations at different image sensor temperatures, each deviation beingcalculated for one image sensor temperature, between the value of thepixel of the image of the image series corresponding to the image sensortemperature and the noise correction factor defined for the pixel at theaverage integration time and at the image sensor temperature, andcomparing the average of deviations to a threshold value, and if theaverage of deviations is greater than the threshold value for a pixel,considering the pixel as defective.

According to an embodiment, the method comprises a step of: correctingeach image acquired by the image sensor by replacing the value of adefective pixel with a value of a neighboring pixel or an average valueof neighboring pixels, or correcting each image acquired by the imagesensor by replacing the respective values of a defective pixel andpixels neighboring the defective pixel with the values of a pixelneighboring the defective pixel and the pixels neighboring the defectivepixel or an average value of pixels neighboring the defective pixel andthe pixels neighboring the defective pixel.

Embodiments may also relate to a device for correcting defects appearingin an image produced by an image sensor, configured to implement thepreviously defined method.

Embodiments may also relate to an imaging device comprising an imagesensor, a circuit for acquiring a temperature of the image sensor and acircuit for acquiring an integration time applied to the image sensor,configured to implement the previously defined method.

According to an embodiment, the image sensor is of a CTIA or SF type.

Embodiments may also relate to a computer program product loadable intothe memory of a computer, which, when executed by the computer,configures the computer to perform the previously defined method.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the disclosure will become more clearlyapparent from the following description provided for exemplary purposesonly and represented in the appended drawings, in which:

FIG. 1 schematically represents a conventional imaging device;

FIG. 2 schematically represents an image sensor associated with a noisereduction device for an imaging device, according to an embodiment;

FIG. 3 schematically illustrates a method for calculating noisereduction coefficients, according to an embodiment;

FIGS. 4 to 6 represent curves of variation in intensity of pixel signalsas a function of the temperature of the image sensor;

FIG. 7 schematically represents a block diagram of a gain correctioncircuit receiving the images from the noise reduction device, accordingto an embodiment; and

FIG. 8 schematically illustrates a block diagram of a circuit forgenerating tables of gain correction coefficients, according to anembodiment.

DETAILED DESCRIPTION

FIG. 1 through FIG. 8, discussed below, and the various embodiments usedto describe the principles of the present disclosure in this patentdocument are by way of illustration only and should not be construed inany way to limit the scope of the disclosure. Those skilled in the artwill understand that the principles of the present disclosure may beimplemented in any suitably arranged system or device.

FIG. 1 shows an image sensor IS1 with processing circuitry ADC, AMP andPRC. The image sensor IS1 may be integrated into a portable device suchas a camera, camcorder, cell phone, or any other device with an imagecapture function. The image sensor IS1 typically includes an array PXAof pixel circuits PC. The array PXA comprises pixel circuits PC arrangedin a plurality of rows and a plurality of columns. The image sensor IS1also includes control circuitry RDRV, RDEC, CDRV, CDEC, TAC, configuredto provide different control signals to the pixel circuits PC accordingto different phases to be chained to capture images. Readout andprocessing circuitry may include an amplifier AMP, an analog-to-digitalconverter ADC and a processor PRC. The array PXA provides pixel signalsto the readout and processing circuitry AMP, ADC, PRC, configured toprovide images IM from the pixel signals.

Pixel rows are selectively activated by a row driver circuit RDRV inresponse to a row address decoder RDEC. The pixel columns are in turnactivated by a column driver circuit controlled by a column decoderCDEC. The circuits RDRV and CDRV provide the appropriate voltages todrive the pixel circuits PC. The sensor IS also includes a controlcircuit TAC that drives the address decoders RDEC and CDEC and the drivecircuits RDRV and CDRV to select the appropriate pixel row and columnfor pixel readout at any given time. The read pixel signals areamplified by the amplifier AMP, and converted to digital by theanalog-to-digital converter ADC. The digital converted pixel signals areprocessed by the image processor PRC, which provides an image IM fromthe digital converted pixel signals. The image processor PRC may includememories to process and store the received image signals.

FIG. 2 shows an image sensor IS associated with a noise reductiondevice, according to an embodiment. In the example of FIG. 2, the noisereduction device includes a noise reduction function implemented by aprocessor IPRC coupled to a memory MEM. The processor IPRC may beintegrated with or connected to the image sensor IS. The definition ofthe noise reduction function is based on a noise model with twocomponents, namely a bias component that varies linearly with thetemperature of the image sensor, and a dark current component thatvaries with both temperature and integration time.

According to an embodiment, the noise model implemented by the processorIPRC is defined by the following equation:

bm[i,j]=IT×ad[i,j]×Exp(bd[i,j]×TP)+ab[i,j]×TP+bb[i,j]  (1)

where bm[i,j] is a pixel of a correction image BM to be subtracted fromeach image produced by the image sensor IS, the position of the pixelbeing identified by row and column indices i, j, IT is the integrationtime, TP is the temperature measured by the image sensor IS or atemperature sensor TS coupled to the image sensor, ad[i,j], bd[i,j],ab[i,j] and bb[i,j] are coefficients determined for the pixel to becorrected at position [i,j], and EXP is the exponential function. Thecoefficients ad[i,j], bd[i,j], ab[i,j] and bb[i,j] are input and storedas tables AD, BD, AB, BB in the memory MEM of the imaging device. Eachpixel px[i,j] of an image IM generated by the image sensor is correctedto produce a corrected image OIM by applying the following equation:

px′[i,j]=px(j,j)−bm[i,j]  (2)

where px′[i,j] is the corrected value of the pixel px[i,j].

Under these conditions, the pixel values provided by the image sensor IScan be individually corrected for all integration times and operatingand imaging device temperatures. Because of the simplicity of thecorrection by subtracting from each pixel px[i,j] of the image IM thecorresponding pixel bm[i,j] of the correction image BM, the imagesprovided by the image sensor IS can be processed at very high framerates. Thus, the processor IPRC can process video streams, includingvideo streams at several hundred frames per second. If necessary, thiscorrection may be performed by a hard-wired logic circuit (as shown inFIG. 2). In addition, all pixels of each frame can be processedsimultaneously in parallel, by providing such a circuit for each pixelof the frame.

Similarly, due to the simplicity of equation (1) involving onlyaddition, multiplication and exponentiation EXP operations, thecalculation of the correction image BM can be performed in real timefollowing a change in temperature TP or in integration time IT. Ifnecessary, the update function of the correction image BM executed bythe processor IPRC may also be performed by a hard-wired logic circuit(as shown in FIG. 2), while the exponentiation operation EXP can beperformed simply by a lookup table. All pixels bm[i,j] of the correctionimage BM can also be computed in parallel, knowing that there is nointeraction between the pixels of the image BM.

FIG. 3 represents a generation circuit TGC of the coefficient tables AD,BD, AB, BB. The circuit TGC receives as input dark images DIM[IT0-ITn,TP1-TPm] obtained using the imaging device, during a calibration phase,by subjecting the image sensor IS to different temperatures TP1, TP2, .. . TPm, and for each temperature, successively setting the integrationtime IT to different values IT0, IT1, . . . ITn, including a minimumvalue IT0. The temperatures TP1-TPm can be selected within the operatingtemperature range of the image sensor IS. In addition, the number ofintegration time values can be set to a number between 5 and 15 withinthe range of possible integration time values of the imaging device.

In a first step, the bias coefficients ab[i,j], bb[i,j] are calculatedfrom the images DIM[IT0, TP1-TPm] obtained with the minimum integrationtime IT0, where in equation (1) the integration time IT is amultiplicative factor in the dark signal component, and therefore thiscomponent becomes negligible when the integration time is very small.According to an example, the minimum integration time IT0 is between 10and 100 μs, and preferably between 40 and 60 μs. As a firstapproximation, each pixel dx[ij,IT0,TP] of the images DIM[IT0, TP1-TPm]obtained with the integration time IT0 may be modeled by the followingequation:

dx[ij,IT0,TP]≅ab[i,j]×TP+bb[i,j]  (3)

with TP=TP1, TP2, . . . TPm. Thus, the bias coefficients ab[i,j] andbb[i,j] from the tables AB, BB can be determined for each pixeldx[ij,IT0,TP], in the temperature range TP1-TPm, by a linear regressioncalculation LR.

In a second step, the dark coefficients ad[i,j], bd[i,j] of the tablesAD, BD are calculated. For this purpose, corrected images DIM′[IT1-ITn,TP1-TPm] are derived from the images DIM[IT1-ITn, TP1-TPm] bysubtracting from each pixel dx[i,j,IT,TP] of each image DIM[IT, TP]obtained for the integration times IT1-ITn and the temperatures TP1-TPm,the value of the corresponding pixel dx[ij,IT0,TP] located in the imageDIM[IT0, TP] obtained for the same temperature TP and for the minimumintegration time IT0. Thus, considering equation (1), each pixeldx′[i,j,IT,TP] of the corrected images DIM[IT1-ITn, TP1-TPm] may bemodeled by the following equation:

dx′[i,j,IT,TP]=IT×ad[i,j,IT]×Exp(bd[i,j,IT]×TP)  (4)

with dx′ [i,j,IT,TP]=dx[i,j,IT,TP]−dx[i,j,IT0,TP].

The dark coefficients ad[i,j] and bd[i,j] can be determined for eachintegration time IT1-ITn by an exponential fitting calculation EF. Sucha calculation is implemented, for example, in the MathWorks® MATLABsoftware library. The dark coefficients ad[i,j] and bd[i,j] stored inthe tables AD, BD can be obtained by averaging the coefficientsad[i,j,IT] and bd[i,j,IT] obtained with the integration times IT1-ITn,respectively.

Depending on the image sensors, it may be suitable to add an identicalcompensation component to the noise model for all pixels of the imagesensor, depending on the integration time. This component can bedetermined by comparing the pixels at a position [i,j] in the images DIMobtained at the same temperature TP for the integration times IT0-ITn.This comparison can be performed by considering a few pixels in each ofthe images DIM obtained at the same temperature TP, for the differentintegration times IT0-ITn, the compensation component being set at anaverage value of the values obtained for the considered pixels.

Each of FIGS. 4 to 6 represents a curve C2, C4, C6 of variation of thevalue of a pixel px[i,j] as a function of temperature TP inuncompensated images IM provided by the image sensor IS, and a curve C1,C3, C5 corresponding to the model bm[i,j] defined by equation (1), anddetermined for pixel [i,j] by the coefficients ad[i,j], bd[i,j], ab[i,j]and bb[i,j]. The curves C2, C4, C6 were plotted from pixel valuespx[i,j] obtained at temperatures TP1≅27° C., TP2≅32° C., TP3≅36.5° C.and TP4≅41° C.

Curves C1, C2 in FIG. 4 correspond to an integration time of 50 μs. FIG.4 shows a difference of less than 0.6% between the value of the pixelpx[i,j] at the output of the image sensor IS and the corresponding valuebm[i,j] determined by the model (equation (1)).

The C3-C6 curves in FIGS. 5 and 6 were obtained with integration timesof 6.654 ms and 13.321 ms, respectively. FIG. 5 shows a maximumdeviation between the measured value of the pixel px[i,j] and thecorresponding value bm[i,j] determined by the model of less than 14%.FIG. 6 shows a maximum deviation of less than 8% between the value ofpixel px[i,j] output by the image sensor IS and the corresponding valuebm[i,j] determined by the model. It can be observed that these maximumdeviations are obtained at temperatures above 38° C.

According to an embodiment, the processor IPRC periodically orcontinuously receives the temperature TP from the image sensor IS, andeach time the processor IPRC calculates the correction image BM, itstores the current temperature measured by the image sensor IS. When thecurrent value of the temperature measurement provided by the imagesensor deviates from the stored value, the processor IPRC recalculatesthe correction image BM taking into account the last temperaturemeasurement provided by the image sensor. Depending on theimplementation method, a new calculation of the BM correction image isperformed when a temperature difference of between 0.5° C. and 2° C.occurs, e.g. 1° C. Similarly, when the IT integration time is changed,the IPRC processor recalculates the BM correction image according to thenew integration time.

According to an embodiment, the image sensor IS has several gain valuesthat can be selected from a control interface of the imaging device oraccording to the illumination conditions of the image sensor. In thiscase, dark images DIM are generated for each gain value to determinetables of coefficients AD, BD, AB, BB for each gain value. Theintegration time values IT used to generate the images DIM can beselected based on the gain values to avoid the unlikely cases where therisk of saturation of the image sensor IS is high. In addition, theprocessor IPRC calculates a current correction image BM for each gainvalue, based on the integration time IT and the temperature of the imagesensor IS.

The quality of the images produced by an image sensor can also bedegraded due to a variation in gain from one pixel to another, and as afunction of the temperature of the image sensor. The variation in gainfrom one pixel to another can result in particular from structuraldifferences between the pixel circuits.

FIG. 7 shows a gain correction circuit, according to an embodiment. Thegain correction circuit receives the corrected image OIM at the outputof the noise reduction circuit and multiplies each pixel px′[i,j] of thecorrected image OIM by a gain correction factor gf[i,j] calculated forpixel [i,j] as a function of the current temperature of the imagesensor. The pixels px″[i,j] of the resulting corrected image GCI werethus produced with a gain that is uniform throughout the image GCI.

The gain correction factor gf[i,j] belongs to a gain normalization tableGF[i,j] stored in memory MEM and determined by an interpolation moduleITP as a function of the temperature TP of the image sensor and a set ofgain normalization tables GF[TP1], . . . GF[TPm] determined for varioustemperatures TP1, . . . TPm. The interpolation applied by theinterpolation module ITP may be, for example, a linear interpolation ora polynomial interpolation.

According to an embodiment, the gain normalization table GF is updatedwhen a temperature difference of between 0.5° C. and 2° C., for example1° C., occurs with respect to the temperature previously taken intoaccount for the calculation of the correction table.

The image processing illustrated in FIG. 7 may be implemented by theprocessor IPRC to process video streams, including video streams atseveral hundred frames per second. If necessary, this processing may beperformed by a hard-wired logic circuit (as shown in FIG. 7). Inaddition, all pixels of each frame can be processed simultaneously inparallel, by providing such a circuit for each pixel of the frame.

Similarly, due to the simplicity of the processing involving onlyaddition, multiplication and inversion operations, the calculation ofthe gain normalization table GF can be performed in real time followinga temperature variation. If necessary, the function for updating thegain normalization table GF executed by the processor IPRC may also beperformed by a hard-wired logic circuit (as shown in FIG. 7), knowingthat, if necessary, the interpolation operation can be performed atleast partly using one or more lookup tables. All pixels gf[i,j] of thegain normalization table GF may also be calculated in parallel.

FIG. 8 represents a circuit GGC for generating the gain normalizationtables GF[TP1], . . . GF[TPm], according to an embodiment. The computingcircuit receives as input images UIM[IT0 . . . ITn, TP1 . . . TPm]obtained during a calibration phase, using the imaging device placed infront of a light source having an apparent uniform light intensity inall directions. The images UIM[IT0 . . . ITn, TP1 . . . TPm] wereobtained by subjecting the image sensor IS to different temperaturesTP1, TPm, and for each temperature, by successively setting theintegration time IT to different values IT0, . . . ITn. The light sourceused to generate the images UIM[IT0 . . . ITn, TP1 . . . TPm] may be anintegrating sphere, the imaging device being placed inside the openingof the integrating sphere. Instead of varying the integration time IT,it is also possible to vary the light intensity emitted by the uniformlight source, by setting the integration time of the image sensor, forexample to an average value. The temperatures TP1, TPm used are, forexample, set at 20, 30, 40 and 50° C.

The circuit GGC calculates a gain table PG[TP] for each of thetemperatures TP1-TPm, using an linear regression calculation circuit RL,the gain pg[i,j,TP] of each pixel [i,j] corresponding to the averageslope of the curve of the value of pixel px[i,j] as a function of theintegration time IT. By computing an average AV, the circuit GGC thendetermines for each temperature TP=TP1, TPm, a table of average gainsPGM[TP], including an average gain value for each pixel [i,j]. Each gainnormalization table GF[TP1], . . . GF[TPm] is then generated bydividing, for each pixel [i,j], the corresponding value pgm[i,j] in theaverage gain table PGM[TP] by the corresponding value pg[i,j,TP] in thegain table PG[TP].

The quality of the images provided by the image sensor IS may also beaffected by the presence of defective pixel circuits. According to anembodiment, the processor IPRC is configured to calculate, for eachpixel [i,j], an average of deviations E[i,j] at different temperaturesTP=TP1, . . . TPm, between the pixel value dx[i,j,IT,TP] of the imagesDIM provided by the image sensor IS at an average integration timeIT_(moy) and the correction value bm[i,j,IT,TP] for this pixel, and tocompare this average to a threshold value. If this average deviation isgreater than the threshold value for a pixel [i,j], pixel [i,j] isconsidered defective. The detection of defective pixels can be performedduring the calibration phase following the acquisition of the imagesDIM. For example, the average deviation E[i,j] can be calculated foreach pixel [i,j] by the following equation:

E[i,j]=[E

(dx[i,j,IT_(moy) ,TP]−bm[i,j,IT_(moy) ,TP])²]^(1/2)  (5)

with TP=TP1, TP2 . . . TPm.

According to an embodiment, the processor IPRC performs a correction ofthe image OIM or GCI by replacing the value px′[i,j] or p″[i,j] of eachpixel thus detected defective by the value of a neighboring pixel, or anaverage value of pixels neighboring the defective pixel. Pixelsneighboring a defective pixel may also be considered defective and forma defective pixel area. In this case, each pixel of the defective pixelarea can be replaced by a neighboring pixel of the defective pixel areaor an average value of these neighboring pixels.

It will become clear to those skilled in the art that the presentdisclosure is susceptible to variations and various applications. Inparticular, the disclosure is not limited to an image sensor performingthe calculation of the correction image BM. Indeed, the correction imageBM can be determined by an external computer having the tables AB, BB,AD, BD and receiving from the image sensor the integration time IT andthe temperature TP of the image sensor IS, and if necessary, the gainapplied by the image sensor.

Moreover, the correction of the images provided by the image sensor mayalso be performed by such an external computer receiving theuncompensated images IM from the image sensor.

In addition, the temperature TP is not necessarily provided by the imagesensor IS, but can be measured by an external temperature sensorassociated with the image sensor.

The processed images can be still images or video images.

Pixel gain correction may be performed from corrected images OIMobtained by other noise reduction processes, so that the gain correctioncan be implemented without performing the noise reduction correctiondescribed with reference to FIG. 2.

Similarly, the detection of defective pixels as described above can beperformed without using any of the correction methods illustrated inFIGS. 2 and 7, performed to obtain the images OIM and GCI. Similarly,the correction of defective pixels can be performed on an uncorrectedimage, i.e., without using either of the correction methods shown inFIGS. 2 and 7.

What is claimed is:
 1. A method of correcting defects appearing in animage produced by an image sensor, the method comprising: receiving animage to be corrected, taken by the image sensor; receiving atemperature from the image sensor, acquired when the image to becorrected is taken by the image sensor; receiving an integration timeapplied by the image sensor when taking the image to be corrected; andfor each pixel of the image to be corrected, subtracting from a pixelvalue a pixel-specific noise correction factor derived from a noisereduction model comprising a linear component dependent on a temperatureof the image sensor, added to an exponential component depending on thetemperature of the image sensor and multiplied by the integration time,the linear and exponential components depending on coefficients specificto the pixel.
 2. The method according to claim 1, wherein the noisereduction model is defined by the following equation:bm[i,j]=IT×ad[i,j]×Exp(bd[i,j]×TP)+ab[i,j]×TP+bb[i,j] where bm[i,j] isthe noise correction factor to be subtracted from a corresponding pixelof the image to be corrected, IT is the integration time, TP is thetemperature of the image sensor, Exp is the exponential function, andad[i,j], bd[i,j], ab[i,j] and bb[i,j] are the pixel-specificcoefficients.
 3. The method according to claim 1, comprising calculatingthe noise correction factor of each pixel each time the integration timeis changed, or each time the temperature of the image sensor deviatesfrom a previous value by more than a temperature deviation thresholdvalue.
 4. The method according to claim 1, comprising: acquiring by theimage sensor images in the absence of light with a minimum integrationtime, each image being taken at a distinct respective temperature; anddetermining, for each pixel of the image to be corrected, coefficientsof the linear component, by linear regression calculations applied tocorresponding pixels in the images in the absence of light taken at therespective temperatures.
 5. The method according to claim 4, comprising:acquiring by the image sensor images in the absence of light, atdifferent integration times, the image sensor being subjected todifferent temperatures; generating corrected images obtained bysubtracting from each of the images taken by the image sensor in theabsence of light at the different integration times and at the differenttemperatures, the image taken by the image sensor in the absence oflight with the minimum integration time and at the same temperature; anddetermining coefficients of the exponential component by exponentialfitting calculations applied to the respective pixels of the correctedimages obtained at the different temperatures and corresponding to thesame integration time.
 6. The method according to claim 5, wherein thecoefficients of the exponential component are determined by averagingcoefficients obtained by exponential fitting calculations for differentintegration times.
 7. The method according to claim 1, wherein the noisereduction model comprises an identical component for all pixels of theimage sensor depending on the integration time.
 8. The method accordingto claim 1, comprising acquiring a stream of video images, wherein thenoise correction factor corresponding to each pixel of an image of thevideo stream is subtracted from the corresponding pixel of each image ofthe video stream.
 9. The method according to claim 1, comprising:receiving a command to select a gain value of the image sensor; andselecting for each pixel of the image to be corrected a pixel-specificnoise correction factor as a function of the selected gain value, thenoise correction factor being used to correct the value of each pixel ofthe image to be corrected, the noise correction factor being determinedfrom a set of pixel-specific coefficients generated as a function of theselected gain value.
 10. The method according to claim 1, comprising,for each pixel of a corrected image after a noise reduction, multiplyingthe value of the pixel by a gain correction factor specific to thepixel, taken from a gain normalization table, to obtain an image with anormalized gain.
 11. The method according to claim 10, comprisingupdating the gain normalization table each time the integration time ischanged, or each time the temperature of the image sensor deviates froma previous value by more than a temperature deviation threshold value,the updating of the gain normalization table being carried out byinterpolation calculations applied to a set of gain normalization tablesdetermined for different temperatures.
 12. The method according to claim11, comprising: acquiring by the image sensor a series of images in thepresence of a uniform light source with different integration times orat different intensities of the light source, each series of imagesbeing taken at a different respective temperature; determining a gain,for each pixel of one of the images of each image series, by a linearregression calculation applied to corresponding pixels in the images ofthe image series; and determining, for each pixel of one of the imagesof each image series, a gain correction factor by dividing an average ofthe gains obtained for all the pixels of the image series by the gaindetermined for the pixel.
 13. The method according to claim 1,comprising: acquiring by the image sensor a series of images in theabsence of light, at an average integration time, each image beingobtained with the image sensor subjected to a respective temperature;calculating, for each pixel of one of the images of the acquired imageseries, an average of deviations at different image sensor temperatures,each deviation being calculated for one image sensor temperature,between the value of the pixel of the image of the image seriescorresponding to the image sensor temperature and the noise correctionfactor defined for the pixel at the average integration time and at theimage sensor temperature; and comparing the average of deviations to athreshold value, and if the average of deviations is greater than thethreshold value for a pixel, considering the pixel as defective.
 14. Themethod according to claim 13, comprising: correcting each image acquiredby the image sensor by replacing the value of a defective pixel with avalue of a neighboring pixel or an average value of neighboring pixels;or correcting each image acquired by the image sensor by replacing therespective values of a defective pixel and pixels neighboring thedefective pixel with the value of a pixel neighboring the defectivepixel and the pixels neighboring the defective pixel or an average valueof pixels neighboring the defective pixel and the pixels neighboring thedefective pixel.
 15. A device for correcting defects appearing in animage produced by an image sensor, the device comprising: a processor;and memory coupled to the processor, the memory comprising instructionsthat, when executed by the processor, cause the device to: receive animage to be corrected, taken by the image sensor; receive a temperaturefrom the image sensor, acquired when the image to be corrected is takenby the image sensor; receive an integration time applied by the imagesensor when taking the image to be corrected; and for each pixel of theimage to be corrected, subtract from a pixel value a pixel-specificnoise correction factor derived from a noise reduction model comprisinga linear component dependent on a temperature of the image sensor, addedto an exponential component depending on the temperature of the imagesensor and multiplied by the integration time, the linear andexponential components depending on coefficients specific to the pixel.16. An imaging device comprising: an image sensor; a circuit foracquiring a temperature of the image sensor; a circuit for acquiring anintegration time applied to the image sensor; a processor; and memorycoupled to the processor, the memory comprising instructions that, whenexecuted by the processor, cause the device to: receive an image to becorrected, taken by the image sensor; receive a temperature from theimage sensor, acquired when the image to be corrected is taken by theimage sensor; receive an integration time applied by the image sensorwhen taking the image to be corrected; and for each pixel of the imageto be corrected, subtract from a pixel value a pixel-specific noisecorrection factor derived from a noise reduction model comprising alinear component dependent on a temperature of the image sensor, addedto an exponential component depending on the temperature of the imagesensor and multiplied by the integration time, the linear andexponential components depending on coefficients specific to the pixel.17. A device according to claim 15, wherein the image sensor is of aCharge Trans Impedance Amplifier (CTIA) or Source Follower (SF) type.18. A device according to claim 16, wherein the image sensor is of aCharge Trans Impedance Amplifier (CTIA) or Source Follower (SF) type.19. A non-transitory computer-readable medium carrying one or moresequences of instructions, which, when executed by one or moreprocessors, causes the one or more processors to: receive an image to becorrected, taken by an image sensor; receive a temperature from theimage sensor, acquired when the image to be corrected is taken by theimage sensor; receive an integration time applied by the image sensorwhen taking the image to be corrected; and for each pixel of the imageto be corrected, subtract from a pixel value a pixel-specific noisecorrection factor derived from a noise reduction model comprising alinear component dependent on a temperature of the image sensor, addedto an exponential component depending on the temperature of the imagesensor and multiplied by the integration time, the linear andexponential components depending on coefficients specific to the pixel.